الملخص العربى
SUMMARY
INTRODUCTION
IntroductIon
Claification and Generalization Capabilities of MultiLayer Feedforward Neural Network.
objective of the Thesis
The Organization of the Thesis
REVIEW ON NEURAL NETWORKS
Introduction
The Structure of The Brain
The Artificial Neuron
The Multilayer Perceptron
Learning Paradigms
A NEW LEARNING ALGORITHM FOR TWO-LAYER NEURAL NETWORKS IN A BINARY FIELD
Introduction
The Leaet Upper Bound (LUB) on The Number Neurons
The Proposed Learning Algorithm
DISCUSSION
Comparison With Othe" ~ " Similar Works
Conclusion
DESIGN OF NEURAL -BASED CODE CONVERTERS
Introduction
IntroductIon to Code Conversion
Neural-Based Code Converter Architectures
Conclusion
THE GENERALIZATION CAPABILITY OF THE PROPOSED ALGORITHM
Introduction
Generalization Ability or Boolean Networks
Generalization Ability of The Proposed Algorithm
Conclusion
NEW DESIGN APPROACHES FOR NEURALBASED ANALOGUE -TO-DIGITAL CONVERTERS •
Introduction
Synthesis by n-Layer Feedforward Neural Network
Syntheeis by 1-Layer Feedforward Neural Network
The Proposed Schemes
Synthesls by m-layer Feedforward Neural Network ( The Second Method)
Comparison With Other Methods
Conclusion
CONCLUSION
LIST OF REFERENCES
APPENDIX
PUBLISHED PAPERS
الفهرس 
